Parallel Local Search on GPU

www.lifl.fr/~luongLocal search algorithms are a class of algorithms to solve complex optimization problems in science and industry. Even if these metaheuristics allow to significantly reduce the computational time of the solution exploration space, the iterative process remains costly when very large problem instances are dealt with. As a solution, graphics processing units (GPUs) represent an efficient alternative for calculations instead of traditional CPU. This paper presents a new methodology to design and implement local search algorithms on GPU. Methods such as tabu search, hill climbing or iterated local search present similar concepts that can be parallelized on GPU and then a general cooperative model can be highlighted. In addition to single-solution based metaheuristics on GPU, this model can be extended with a hybrid multi-core and multi-GPU approach for multiple local search methods such as multistart. The conclusions from both GPU and multi-GPU experiments indicate significant speed-ups compared to CPU approaches

GPU Computing for Parallel Local Search Metaheuristics

International audienceLocal search metaheuristics (LSMs) are efficient methods for solving complex problems in science and industry. They allow significantly to reduce the size of the search space to be explored and the search time. Nevertheless, the resolution time remains prohibitive when dealing with large problem instances. Therefore, the use of GPU-based massively parallel computing is a major complementary way to speed up the search. However, GPU computing for LSMs is rarely investigated in the literature. In this paper, we introduce a new guideline for the design and implementation of effective LSMs on GPU. Very efficient approaches are proposed for CPU-GPU data transfer optimization, thread control, mapping of neighboring solutions to GPU threads and memory management. These approaches have been experimented using four well-known combinatorial and continuous optimization problems and four GPU configurations. Compared to a CPU-based execution, accelerations up to x80 are reported for the large combinatorial problems and up to x240 for a continuous problem. Finally, extensive experiments demonstrate the strong potential of GPU-based LSMs compared to cluster or grid-based parallel architectures

Parallel local search for solving Constraint Problems on the Cell Broadband Engine (Preliminary Results)

We explore the use of the Cell Broadband Engine (Cell/BE for short) for combinatorial optimization applications: we present a parallel version of a constraint-based local search algorithm that has been implemented on a multiprocessor BladeCenter machine with twin Cell/BE processors (total of 16 SPUs per blade). This algorithm was chosen because it fits very well the Cell/BE architecture and requires neither shared memory nor communication between processors, while retaining a compact memory footprint. We study the performance on several large optimization benchmarks and show that this achieves mostly linear time speedups, even sometimes super-linear. This is possible because the parallel implementation might explore simultaneously different parts of the search space and therefore converge faster towards the best sub-space and thus towards a solution. Besides getting speedups, the resulting times exhibit a much smaller variance, which benefits applications where a timely reply is critical

Solving Stable Matching Problems via Cooperative Parallel Local Search

International audienceStable matching problems and its variants have several practical applications, like the Hospital/Residents problem, stable roommates problem or bipartite market sharing. An important generalization problem is the SMTI which allows for incompleteness and ties in the user's preference lists. Finding a maximal size stable matching for SMTI is compu-tationally difficult. We developed a Local Search method to solve SMTI using the Adaptive Search algorithm and present experimental evidence that this approach is much more efficient than state-of-the-art exact and approximate methods (in terms of both computational effort required and quality of solution). We also tried a parallel version of our algorithm. For this we reused the Cooperative Parallel Local Search framework (CPLS) we designed. CPLS is a highly parametric framework for the execution in parallel of local search solvers allowing them to cooperate though communication. The cooperative parallel version of our local search algorithm improves performance so much that very large and hard instances can be solved quickly

Parallel local search for the time-constrained traveling salesman problem

In the time-constrained TSP, each city has to be visited within a given time interval. Such `time windows' often occur in practice. When practical vehicle routing problems are solved in an interactive setting, one needs algorithms for the time-constrained TSP that combine a low running time with a high solution quality. Local search seems a natural approach. It is not obvious, however, how local search for the TSP has to be implemented so as to handle time windows efficiently. This is particularly true when parallel computer architectures are available. We consider these questions

Evolutionary strategy based improved motion estimation technique for H.264 video coding

In this paper we propose an improved motion estimation algorithm based on evolutionary strategy (ES) for H.264 video codec applied to video. The proposed technique works in a parallel local search for macroblocks. For this purpose (mu+lambda) ES is used with an initial population of heuristically and randomly generated motion vectors. Experimental results show that the proposed scheme can reduce the computational complexity up to 50% of the motion estimation algorithm used in the H.264 reference codec at the same picture quality. Therefore, the proposed algorithm provides a significant improvement in motion estimation in the H.264 video codec

Sequential and parallel local search for the time-constrained travelling salesman problem

Local search has proven to be an effective solution approach for the traveling salesman problem. We consider variants of the TSP in which each city is to be visited within one or more given time windows. The travel times are symmetric and satisfy the triangle inequality; therobjective is to minimize the tour duration. We develop efficient sequential and parallel algorithms for the verification of local optimality of a tour with respect to k-exchanges

An evolutionary strategy based motion estimation algorithm for H.264 video codecs

In this paper, we propose a new motion estimation algorithm based on evolutionary strategy (ES) for the H.264 video codec applied to monoscopic video. The proposed technique applies in macroblock basis and performs a parallel local search for the motion vector associated with the minimum motion compensated residue. For this purpose (/spl mu/+/spl lambda/)-ES is used with heuristically and randomly generated population of initial motion vectors. Experimental results show that the proposed scheme can reduce the computational complexity up to 50% of the motion estimation algorithm used in the H.264 reference codec at the same picture quality. Therefore, the proposed algorithm provides a significant improvement in motion estimation in the H.264 video codec